Overview

Brought to you by YData

Dataset statistics

Number of variables17
Number of observations4617
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory613.3 KiB
Average record size in memory136.0 B

Variable types

Numeric14
Categorical3

Alerts

Day_Charge is highly overall correlated with Day_MinsHigh correlation
Day_Mins is highly overall correlated with Day_ChargeHigh correlation
Eve_Charge is highly overall correlated with Eve_MinsHigh correlation
Eve_Mins is highly overall correlated with Eve_ChargeHigh correlation
International_Charge is highly overall correlated with International_MinsHigh correlation
International_Mins is highly overall correlated with International_ChargeHigh correlation
Night_Charge is highly overall correlated with Night_MinsHigh correlation
Night_Mins is highly overall correlated with Night_ChargeHigh correlation
International_Plan is highly imbalanced (54.2%) Imbalance
CustServ_Calls has 951 (20.6%) zeros Zeros

Reproduction

Analysis started2025-09-21 13:04:25.855374
Analysis finished2025-09-21 13:04:54.339691
Duration28.48 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Account_Length
Real number (ℝ)

Distinct218
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.64522
Minimum1
Maximum243
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.2 KiB
2025-09-21T13:04:54.442187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile36
Q174
median100
Q3127
95-th percentile167
Maximum243
Range242
Interquartile range (IQR)53

Descriptive statistics

Standard deviation39.597194
Coefficient of variation (CV)0.39343342
Kurtosis-0.092552247
Mean100.64522
Median Absolute Deviation (MAD)27
Skewness0.10624769
Sum464679
Variance1567.9378
MonotonicityNot monotonic
2025-09-21T13:04:54.576759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 63
 
1.4%
87 56
 
1.2%
93 54
 
1.2%
105 54
 
1.2%
100 53
 
1.1%
112 53
 
1.1%
101 53
 
1.1%
103 50
 
1.1%
94 50
 
1.1%
78 49
 
1.1%
Other values (208) 4082
88.4%
ValueCountFrequency (%)
1 10
0.2%
2 1
 
< 0.1%
3 8
0.2%
4 3
 
0.1%
5 2
 
< 0.1%
6 2
 
< 0.1%
7 4
 
0.1%
8 2
 
< 0.1%
9 3
 
0.1%
10 3
 
0.1%
ValueCountFrequency (%)
243 1
< 0.1%
238 1
< 0.1%
233 1
< 0.1%
232 2
< 0.1%
225 2
< 0.1%
224 2
< 0.1%
222 1
< 0.1%
221 1
< 0.1%
217 2
< 0.1%
216 1
< 0.1%

International_Plan
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size36.2 KiB
no
4171 
yes
446 

Length

Max length4
Median length3
Mean length3.0965995
Min length3

Characters and Unicode

Total characters14297
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row no
2nd row no
3rd row no
4th row yes
5th row yes

Common Values

ValueCountFrequency (%)
no 4171
90.3%
yes 446
 
9.7%

Length

2025-09-21T13:04:54.697724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-21T13:04:54.765931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 4171
90.3%
yes 446
 
9.7%

Most occurring characters

ValueCountFrequency (%)
4617
32.3%
n 4171
29.2%
o 4171
29.2%
y 446
 
3.1%
e 446
 
3.1%
s 446
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14297
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4617
32.3%
n 4171
29.2%
o 4171
29.2%
y 446
 
3.1%
e 446
 
3.1%
s 446
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14297
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4617
32.3%
n 4171
29.2%
o 4171
29.2%
y 446
 
3.1%
e 446
 
3.1%
s 446
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14297
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4617
32.3%
n 4171
29.2%
o 4171
29.2%
y 446
 
3.1%
e 446
 
3.1%
s 446
 
3.1%

VMail_Plan
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size36.2 KiB
no
3381 
yes
1236 

Length

Max length4
Median length3
Mean length3.2677063
Min length3

Characters and Unicode

Total characters15087
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row yes
2nd row yes
3rd row no
4th row no
5th row no

Common Values

ValueCountFrequency (%)
no 3381
73.2%
yes 1236
 
26.8%

Length

2025-09-21T13:04:54.858664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-21T13:04:54.934699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 3381
73.2%
yes 1236
 
26.8%

Most occurring characters

ValueCountFrequency (%)
4617
30.6%
n 3381
22.4%
o 3381
22.4%
y 1236
 
8.2%
e 1236
 
8.2%
s 1236
 
8.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15087
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4617
30.6%
n 3381
22.4%
o 3381
22.4%
y 1236
 
8.2%
e 1236
 
8.2%
s 1236
 
8.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15087
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4617
30.6%
n 3381
22.4%
o 3381
22.4%
y 1236
 
8.2%
e 1236
 
8.2%
s 1236
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15087
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4617
30.6%
n 3381
22.4%
o 3381
22.4%
y 1236
 
8.2%
e 1236
 
8.2%
s 1236
 
8.2%

Day_Mins
Real number (ℝ)

High correlation 

Distinct1901
Distinct (%)41.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180.44715
Minimum0
Maximum351.5
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size36.2 KiB
2025-09-21T13:04:55.035310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile91.66
Q1143.7
median180
Q3216.8
95-th percentile271.1
Maximum351.5
Range351.5
Interquartile range (IQR)73.1

Descriptive statistics

Standard deviation53.98354
Coefficient of variation (CV)0.29916537
Kurtosis-0.042398864
Mean180.44715
Median Absolute Deviation (MAD)36.4
Skewness-0.0029482419
Sum833124.5
Variance2914.2226
MonotonicityNot monotonic
2025-09-21T13:04:55.172708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
154 10
 
0.2%
189.3 10
 
0.2%
184.5 9
 
0.2%
177.1 9
 
0.2%
159.5 9
 
0.2%
180 9
 
0.2%
174.5 9
 
0.2%
143.7 8
 
0.2%
168.6 8
 
0.2%
183.4 8
 
0.2%
Other values (1891) 4528
98.1%
ValueCountFrequency (%)
0 2
< 0.1%
2.6 1
< 0.1%
7.8 1
< 0.1%
7.9 1
< 0.1%
12.5 1
< 0.1%
17.6 1
< 0.1%
18.9 1
< 0.1%
19.5 1
< 0.1%
25.9 1
< 0.1%
27 1
< 0.1%
ValueCountFrequency (%)
351.5 1
< 0.1%
350.8 1
< 0.1%
346.8 1
< 0.1%
345.3 1
< 0.1%
338.4 1
< 0.1%
337.4 1
< 0.1%
335.5 1
< 0.1%
334.3 1
< 0.1%
332.9 1
< 0.1%
332.1 1
< 0.1%

Day_Calls
Real number (ℝ)

Distinct123
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.05436
Minimum0
Maximum165
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size36.2 KiB
2025-09-21T13:04:55.328197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile67
Q187
median100
Q3113
95-th percentile133
Maximum165
Range165
Interquartile range (IQR)26

Descriptive statistics

Standard deviation19.883027
Coefficient of variation (CV)0.19872224
Kurtosis0.19614677
Mean100.05436
Median Absolute Deviation (MAD)13
Skewness-0.081013449
Sum461951
Variance395.33478
MonotonicityNot monotonic
2025-09-21T13:04:55.470334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
102 108
 
2.3%
105 106
 
2.3%
95 101
 
2.2%
97 98
 
2.1%
108 97
 
2.1%
100 96
 
2.1%
94 93
 
2.0%
98 93
 
2.0%
112 93
 
2.0%
92 92
 
2.0%
Other values (113) 3640
78.8%
ValueCountFrequency (%)
0 2
< 0.1%
30 1
 
< 0.1%
34 1
 
< 0.1%
35 1
 
< 0.1%
36 1
 
< 0.1%
39 1
 
< 0.1%
40 2
< 0.1%
42 2
< 0.1%
44 4
0.1%
45 3
0.1%
ValueCountFrequency (%)
165 1
 
< 0.1%
163 1
 
< 0.1%
160 2
 
< 0.1%
158 3
0.1%
157 2
 
< 0.1%
156 3
0.1%
152 2
 
< 0.1%
151 7
0.2%
150 6
0.1%
149 2
 
< 0.1%

Day_Charge
Real number (ℝ)

High correlation 

Distinct1901
Distinct (%)41.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.676576
Minimum0
Maximum59.76
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size36.2 KiB
2025-09-21T13:04:55.605211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15.584
Q124.43
median30.6
Q336.86
95-th percentile46.09
Maximum59.76
Range59.76
Interquartile range (IQR)12.43

Descriptive statistics

Standard deviation9.1771455
Coefficient of variation (CV)0.29915808
Kurtosis-0.042263913
Mean30.676576
Median Absolute Deviation (MAD)6.19
Skewness-0.0029517679
Sum141633.75
Variance84.219999
MonotonicityNot monotonic
2025-09-21T13:04:55.736752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26.18 10
 
0.2%
32.18 10
 
0.2%
31.37 9
 
0.2%
30.11 9
 
0.2%
27.12 9
 
0.2%
30.6 9
 
0.2%
29.67 9
 
0.2%
24.43 8
 
0.2%
28.66 8
 
0.2%
31.18 8
 
0.2%
Other values (1891) 4528
98.1%
ValueCountFrequency (%)
0 2
< 0.1%
0.44 1
< 0.1%
1.33 1
< 0.1%
1.34 1
< 0.1%
2.13 1
< 0.1%
2.99 1
< 0.1%
3.21 1
< 0.1%
3.32 1
< 0.1%
4.4 1
< 0.1%
4.59 1
< 0.1%
ValueCountFrequency (%)
59.76 1
< 0.1%
59.64 1
< 0.1%
58.96 1
< 0.1%
58.7 1
< 0.1%
57.53 1
< 0.1%
57.36 1
< 0.1%
57.04 1
< 0.1%
56.83 1
< 0.1%
56.59 1
< 0.1%
56.46 1
< 0.1%

Eve_Mins
Real number (ℝ)

High correlation 

Distinct1833
Distinct (%)39.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean200.42909
Minimum0
Maximum363.7
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size36.2 KiB
2025-09-21T13:04:55.867885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile118.78
Q1165.9
median200.8
Q3234
95-th percentile284.12
Maximum363.7
Range363.7
Interquartile range (IQR)68.1

Descriptive statistics

Standard deviation50.557001
Coefficient of variation (CV)0.25224383
Kurtosis0.043630155
Mean200.42909
Median Absolute Deviation (MAD)34.1
Skewness-0.0052798656
Sum925381.1
Variance2556.0103
MonotonicityNot monotonic
2025-09-21T13:04:56.001954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
169.9 10
 
0.2%
210.6 9
 
0.2%
230.9 9
 
0.2%
188.8 9
 
0.2%
187.5 9
 
0.2%
223.5 9
 
0.2%
167.6 9
 
0.2%
161.7 9
 
0.2%
194 9
 
0.2%
211.7 8
 
0.2%
Other values (1823) 4527
98.1%
ValueCountFrequency (%)
0 1
< 0.1%
22.3 1
< 0.1%
31.2 1
< 0.1%
37.8 1
< 0.1%
41.7 1
< 0.1%
42.2 1
< 0.1%
42.5 1
< 0.1%
43.9 1
< 0.1%
47.3 1
< 0.1%
48.1 1
< 0.1%
ValueCountFrequency (%)
363.7 1
< 0.1%
361.8 1
< 0.1%
354.2 1
< 0.1%
352.1 1
< 0.1%
351.6 1
< 0.1%
350.9 1
< 0.1%
350.5 1
< 0.1%
349.4 1
< 0.1%
348.9 1
< 0.1%
348.5 1
< 0.1%

Eve_Calls
Real number (ℝ)

Distinct125
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.17977
Minimum0
Maximum170
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size36.2 KiB
2025-09-21T13:04:56.167673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile67
Q187
median101
Q3114
95-th percentile133
Maximum170
Range170
Interquartile range (IQR)27

Descriptive statistics

Standard deviation19.821314
Coefficient of variation (CV)0.19785745
Kurtosis0.13598038
Mean100.17977
Median Absolute Deviation (MAD)13
Skewness-0.017554065
Sum462530
Variance392.88449
MonotonicityNot monotonic
2025-09-21T13:04:57.116534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
105 111
 
2.4%
97 101
 
2.2%
94 99
 
2.1%
103 99
 
2.1%
101 98
 
2.1%
91 97
 
2.1%
102 95
 
2.1%
106 92
 
2.0%
88 91
 
2.0%
96 91
 
2.0%
Other values (115) 3643
78.9%
ValueCountFrequency (%)
0 1
 
< 0.1%
12 1
 
< 0.1%
36 1
 
< 0.1%
37 1
 
< 0.1%
42 1
 
< 0.1%
43 1
 
< 0.1%
44 1
 
< 0.1%
45 1
 
< 0.1%
46 5
0.1%
47 2
 
< 0.1%
ValueCountFrequency (%)
170 1
 
< 0.1%
169 1
 
< 0.1%
168 1
 
< 0.1%
164 1
 
< 0.1%
159 1
 
< 0.1%
157 1
 
< 0.1%
156 1
 
< 0.1%
155 5
0.1%
154 4
0.1%
153 1
 
< 0.1%

Eve_Charge
Real number (ℝ)

High correlation 

Distinct1621
Distinct (%)35.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.036703
Minimum0
Maximum30.91
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size36.2 KiB
2025-09-21T13:04:57.314368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10.098
Q114.1
median17.07
Q319.89
95-th percentile24.152
Maximum30.91
Range30.91
Interquartile range (IQR)5.79

Descriptive statistics

Standard deviation4.2973325
Coefficient of variation (CV)0.25223967
Kurtosis0.043521686
Mean17.036703
Median Absolute Deviation (MAD)2.9
Skewness-0.0052518173
Sum78658.46
Variance18.467066
MonotonicityNot monotonic
2025-09-21T13:04:57.543096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.25 15
 
0.3%
16.12 14
 
0.3%
15.9 14
 
0.3%
18.79 13
 
0.3%
16.8 11
 
0.2%
18.62 11
 
0.2%
17.99 11
 
0.2%
16.97 11
 
0.2%
18.96 11
 
0.2%
13.74 10
 
0.2%
Other values (1611) 4496
97.4%
ValueCountFrequency (%)
0 1
< 0.1%
1.9 1
< 0.1%
2.65 1
< 0.1%
3.21 1
< 0.1%
3.54 1
< 0.1%
3.59 1
< 0.1%
3.61 1
< 0.1%
3.73 1
< 0.1%
4.02 1
< 0.1%
4.09 1
< 0.1%
ValueCountFrequency (%)
30.91 1
< 0.1%
30.75 1
< 0.1%
30.11 1
< 0.1%
29.93 1
< 0.1%
29.89 1
< 0.1%
29.83 1
< 0.1%
29.79 1
< 0.1%
29.7 1
< 0.1%
29.66 1
< 0.1%
29.62 1
< 0.1%

Night_Mins
Real number (ℝ)

High correlation 

Distinct1813
Distinct (%)39.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean200.62393
Minimum23.2
Maximum395
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.2 KiB
2025-09-21T13:04:57.726982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum23.2
5-th percentile117.28
Q1167.1
median200.8
Q3234.9
95-th percentile283.52
Maximum395
Range371.8
Interquartile range (IQR)67.8

Descriptive statistics

Standard deviation50.543616
Coefficient of variation (CV)0.25193213
Kurtosis0.061409178
Mean200.62393
Median Absolute Deviation (MAD)33.9
Skewness0.02051508
Sum926280.7
Variance2554.6571
MonotonicityNot monotonic
2025-09-21T13:04:57.940122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
188.2 10
 
0.2%
214.6 10
 
0.2%
194.3 10
 
0.2%
186.2 10
 
0.2%
214.7 9
 
0.2%
192.7 9
 
0.2%
197.4 9
 
0.2%
181.2 8
 
0.2%
214.4 8
 
0.2%
221.6 8
 
0.2%
Other values (1803) 4526
98.0%
ValueCountFrequency (%)
23.2 1
< 0.1%
43.7 1
< 0.1%
45 1
< 0.1%
46.7 1
< 0.1%
47.4 1
< 0.1%
50.1 2
< 0.1%
50.9 1
< 0.1%
53.3 1
< 0.1%
54 1
< 0.1%
54.5 1
< 0.1%
ValueCountFrequency (%)
395 1
< 0.1%
381.9 1
< 0.1%
381.6 1
< 0.1%
377.5 1
< 0.1%
367.7 1
< 0.1%
364.9 1
< 0.1%
364.3 1
< 0.1%
359.9 1
< 0.1%
354.9 1
< 0.1%
352.5 1
< 0.1%

Night_Calls
Real number (ℝ)

Distinct130
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.94412
Minimum12
Maximum175
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.2 KiB
2025-09-21T13:04:58.145886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile67
Q187
median100
Q3113
95-th percentile132
Maximum175
Range163
Interquartile range (IQR)26

Descriptive statistics

Standard deviation19.935053
Coefficient of variation (CV)0.19946199
Kurtosis0.068815249
Mean99.94412
Median Absolute Deviation (MAD)13
Skewness0.030886305
Sum461442
Variance397.40632
MonotonicityNot monotonic
2025-09-21T13:04:58.377200image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
105 115
 
2.5%
102 102
 
2.2%
104 101
 
2.2%
100 100
 
2.2%
91 97
 
2.1%
103 94
 
2.0%
95 94
 
2.0%
94 92
 
2.0%
99 91
 
2.0%
98 91
 
2.0%
Other values (120) 3640
78.8%
ValueCountFrequency (%)
12 1
 
< 0.1%
33 1
 
< 0.1%
36 1
 
< 0.1%
38 2
< 0.1%
40 1
 
< 0.1%
41 1
 
< 0.1%
42 4
0.1%
43 1
 
< 0.1%
44 1
 
< 0.1%
46 3
0.1%
ValueCountFrequency (%)
175 1
< 0.1%
170 1
< 0.1%
168 1
< 0.1%
166 1
< 0.1%
165 1
< 0.1%
164 1
< 0.1%
161 1
< 0.1%
160 1
< 0.1%
159 2
< 0.1%
158 2
< 0.1%

Night_Charge
Real number (ℝ)

High correlation 

Distinct1012
Distinct (%)21.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.028185
Minimum1.04
Maximum17.77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.2 KiB
2025-09-21T13:04:58.612722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.04
5-th percentile5.278
Q17.52
median9.04
Q310.57
95-th percentile12.76
Maximum17.77
Range16.73
Interquartile range (IQR)3.05

Descriptive statistics

Standard deviation2.2744876
Coefficient of variation (CV)0.25193188
Kurtosis0.06138035
Mean9.028185
Median Absolute Deviation (MAD)1.52
Skewness0.02050698
Sum41683.13
Variance5.1732938
MonotonicityNot monotonic
2025-09-21T13:04:58.798779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.66 19
 
0.4%
8.47 18
 
0.4%
8.15 17
 
0.4%
10.8 17
 
0.4%
9.4 16
 
0.3%
9.45 16
 
0.3%
9.23 15
 
0.3%
8.88 15
 
0.3%
10.49 15
 
0.3%
9.65 15
 
0.3%
Other values (1002) 4454
96.5%
ValueCountFrequency (%)
1.04 1
< 0.1%
1.97 1
< 0.1%
2.03 1
< 0.1%
2.1 1
< 0.1%
2.13 1
< 0.1%
2.25 2
< 0.1%
2.29 1
< 0.1%
2.4 1
< 0.1%
2.43 1
< 0.1%
2.45 1
< 0.1%
ValueCountFrequency (%)
17.77 1
< 0.1%
17.19 1
< 0.1%
17.17 1
< 0.1%
16.99 1
< 0.1%
16.55 1
< 0.1%
16.42 1
< 0.1%
16.39 1
< 0.1%
16.2 1
< 0.1%
15.97 1
< 0.1%
15.86 1
< 0.1%

International_Mins
Real number (ℝ)

High correlation 

Distinct168
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.279294
Minimum0
Maximum20
Zeros23
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size36.2 KiB
2025-09-21T13:04:58.933513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.78
Q18.6
median10.3
Q312.1
95-th percentile14.7
Maximum20
Range20
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation2.7573614
Coefficient of variation (CV)0.26824424
Kurtosis0.67160197
Mean10.279294
Median Absolute Deviation (MAD)1.8
Skewness-0.22089058
Sum47459.5
Variance7.6030417
MonotonicityNot monotonic
2025-09-21T13:04:59.081095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.1 81
 
1.8%
9.8 81
 
1.8%
11.3 77
 
1.7%
10 77
 
1.7%
10.5 75
 
1.6%
10.1 75
 
1.6%
10.6 74
 
1.6%
10.9 74
 
1.6%
11 73
 
1.6%
9.5 72
 
1.6%
Other values (158) 3858
83.6%
ValueCountFrequency (%)
0 23
0.5%
0.4 1
 
< 0.1%
1.1 1
 
< 0.1%
1.3 1
 
< 0.1%
2 2
 
< 0.1%
2.1 2
 
< 0.1%
2.2 2
 
< 0.1%
2.4 1
 
< 0.1%
2.5 1
 
< 0.1%
2.6 1
 
< 0.1%
ValueCountFrequency (%)
20 1
 
< 0.1%
19.7 1
 
< 0.1%
19.3 1
 
< 0.1%
19.2 1
 
< 0.1%
18.9 2
< 0.1%
18.7 1
 
< 0.1%
18.4 1
 
< 0.1%
18.3 1
 
< 0.1%
18.2 2
< 0.1%
18 3
0.1%

International_Calls
Real number (ℝ)

Distinct21
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4338315
Minimum0
Maximum20
Zeros23
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size36.2 KiB
2025-09-21T13:04:59.196775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q36
95-th percentile9
Maximum20
Range20
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4576145
Coefficient of variation (CV)0.55428685
Kurtosis3.3029273
Mean4.4338315
Median Absolute Deviation (MAD)1
Skewness1.3664196
Sum20471
Variance6.039869
MonotonicityNot monotonic
2025-09-21T13:04:59.300541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
3 925
20.0%
4 881
19.1%
2 680
14.7%
5 646
14.0%
6 460
10.0%
7 283
 
6.1%
1 245
 
5.3%
8 156
 
3.4%
9 141
 
3.1%
10 70
 
1.5%
Other values (11) 130
 
2.8%
ValueCountFrequency (%)
0 23
 
0.5%
1 245
 
5.3%
2 680
14.7%
3 925
20.0%
4 881
19.1%
5 646
14.0%
6 460
10.0%
7 283
 
6.1%
8 156
 
3.4%
9 141
 
3.1%
ValueCountFrequency (%)
20 1
 
< 0.1%
19 2
 
< 0.1%
18 4
 
0.1%
17 1
 
< 0.1%
16 6
 
0.1%
15 9
 
0.2%
14 6
 
0.1%
13 19
0.4%
12 21
0.5%
11 38
0.8%

International_Charge
Real number (ℝ)

High correlation 

Distinct168
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7759259
Minimum0
Maximum5.4
Zeros23
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size36.2 KiB
2025-09-21T13:04:59.424437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.564
Q12.32
median2.78
Q33.27
95-th percentile3.97
Maximum5.4
Range5.4
Interquartile range (IQR)0.95

Descriptive statistics

Standard deviation0.74441288
Coefficient of variation (CV)0.26816741
Kurtosis0.67251785
Mean2.7759259
Median Absolute Deviation (MAD)0.48
Skewness-0.22134225
Sum12816.45
Variance0.55415053
MonotonicityNot monotonic
2025-09-21T13:04:59.565471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 81
 
1.8%
2.65 81
 
1.8%
3.05 77
 
1.7%
2.7 77
 
1.7%
2.84 75
 
1.6%
2.73 75
 
1.6%
2.86 74
 
1.6%
2.94 74
 
1.6%
2.97 73
 
1.6%
2.57 72
 
1.6%
Other values (158) 3858
83.6%
ValueCountFrequency (%)
0 23
0.5%
0.11 1
 
< 0.1%
0.3 1
 
< 0.1%
0.35 1
 
< 0.1%
0.54 2
 
< 0.1%
0.57 2
 
< 0.1%
0.59 2
 
< 0.1%
0.65 1
 
< 0.1%
0.68 1
 
< 0.1%
0.7 1
 
< 0.1%
ValueCountFrequency (%)
5.4 1
 
< 0.1%
5.32 1
 
< 0.1%
5.21 1
 
< 0.1%
5.18 1
 
< 0.1%
5.1 2
< 0.1%
5.05 1
 
< 0.1%
4.97 1
 
< 0.1%
4.94 1
 
< 0.1%
4.91 2
< 0.1%
4.86 3
0.1%

CustServ_Calls
Real number (ℝ)

Zeros 

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5670349
Minimum0
Maximum9
Zeros951
Zeros (%)20.6%
Negative0
Negative (%)0.0%
Memory size36.2 KiB
2025-09-21T13:04:59.686222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile4
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3070187
Coefficient of variation (CV)0.83407121
Kurtosis1.5150264
Mean1.5670349
Median Absolute Deviation (MAD)1
Skewness1.0468003
Sum7235
Variance1.7082978
MonotonicityNot monotonic
2025-09-21T13:04:59.769352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 1651
35.8%
2 1031
22.3%
0 951
20.6%
3 616
 
13.3%
4 234
 
5.1%
5 89
 
1.9%
6 28
 
0.6%
7 13
 
0.3%
9 2
 
< 0.1%
8 2
 
< 0.1%
ValueCountFrequency (%)
0 951
20.6%
1 1651
35.8%
2 1031
22.3%
3 616
 
13.3%
4 234
 
5.1%
5 89
 
1.9%
6 28
 
0.6%
7 13
 
0.3%
8 2
 
< 0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
9 2
 
< 0.1%
8 2
 
< 0.1%
7 13
 
0.3%
6 28
 
0.6%
5 89
 
1.9%
4 234
 
5.1%
3 616
 
13.3%
2 1031
22.3%
1 1651
35.8%
0 951
20.6%

Churn
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size36.2 KiB
False.
3961 
True.
656 

Length

Max length7
Median length7
Mean length6.8579164
Min length6

Characters and Unicode

Total characters31663
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row False.
2nd row False.
3rd row False.
4th row False.
5th row False.

Common Values

ValueCountFrequency (%)
False. 3961
85.8%
True. 656
 
14.2%

Length

2025-09-21T13:04:59.865204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-21T13:04:59.929501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
false 3961
85.8%
true 656
 
14.2%

Most occurring characters

ValueCountFrequency (%)
4617
14.6%
e 4617
14.6%
. 4617
14.6%
F 3961
12.5%
l 3961
12.5%
a 3961
12.5%
s 3961
12.5%
T 656
 
2.1%
r 656
 
2.1%
u 656
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 31663
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4617
14.6%
e 4617
14.6%
. 4617
14.6%
F 3961
12.5%
l 3961
12.5%
a 3961
12.5%
s 3961
12.5%
T 656
 
2.1%
r 656
 
2.1%
u 656
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 31663
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4617
14.6%
e 4617
14.6%
. 4617
14.6%
F 3961
12.5%
l 3961
12.5%
a 3961
12.5%
s 3961
12.5%
T 656
 
2.1%
r 656
 
2.1%
u 656
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 31663
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4617
14.6%
e 4617
14.6%
. 4617
14.6%
F 3961
12.5%
l 3961
12.5%
a 3961
12.5%
s 3961
12.5%
T 656
 
2.1%
r 656
 
2.1%
u 656
 
2.1%

Interactions

2025-09-21T13:04:52.504963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:27.392902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:30.167684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:32.408318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:34.328629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:36.199630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:37.817636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:39.414232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:41.381824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:43.007695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:45.248891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:47.105085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:49.286456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:50.942906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:52.608241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:27.502068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:30.282128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:32.576105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:34.433855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:36.311502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:37.926991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:39.539759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:41.490804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:43.118684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:45.416201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:47.221997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:49.398086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:51.050225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:52.714391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:27.611413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:30.402389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:32.748970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:34.542669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:36.436360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:38.038315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:39.648826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:41.623564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:43.241497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:45.578212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:47.346265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:49.518356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:51.173815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:52.817532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:27.715463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:30.523222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:32.926961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:34.649121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:36.552451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:38.151925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:39.759462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:41.735669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:43.358784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:45.746717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:47.465565image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:49.630550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:51.289464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:52.919804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:27.835539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:30.640022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:33.106353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:34.751951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:36.661896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:38.265859image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:39.863225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:41.846572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:43.539787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:45.927191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:47.583068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:49.750541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:51.403407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:53.021097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:27.944982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:30.843620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:33.291119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:35.187158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:36.772073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:38.379666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:39.973647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:41.963269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:43.718607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:46.099677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:47.698946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:49.866128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:51.519859image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:53.128500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:28.055857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:31.036265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:33.423586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:35.318577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:36.883497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:38.506374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:40.079821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:42.075230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:43.894372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:46.213707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:47.815868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:49.988677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:51.624457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:53.248827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:28.164361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:31.204852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:33.533731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:35.423221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:36.992256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:38.619517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:40.185653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:42.188948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:44.058299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:46.327657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:47.941040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-09-21T13:04:53.356349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:28.272052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:31.371432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:33.643377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:35.541563image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:37.113455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:38.733256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:40.298992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:42.305436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:44.237456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:46.437680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:48.070879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:50.239713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:51.837374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:53.462933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:29.611697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:31.564039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-09-21T13:04:37.232515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:38.848680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:40.823963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:42.426301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:44.402187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-09-21T13:04:29.716896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-09-21T13:04:33.867673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:35.760264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:37.352735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:38.957496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:40.935033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:42.541276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:44.566329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:46.656668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:48.799280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:50.480329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:52.052913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:53.674824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:29.830551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:31.899961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:33.981126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:35.876079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:37.491647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:39.082076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:41.053254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:42.677865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:44.742960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:46.768905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:48.922575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:50.598733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:52.185020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:53.785013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:29.958609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:32.084299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:34.097296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:35.990562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:37.607856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:39.201023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:41.170571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:42.794913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:44.918307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:46.895497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:49.062332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:50.715215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:52.303357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:53.881233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:30.067487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:32.248632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:34.206847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:36.092758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:37.713946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:39.311016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:41.276389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:42.904136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:45.077336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:47.004669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:49.176937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:50.828303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T13:04:52.399532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-09-21T13:05:00.010278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Account_LengthChurnCustServ_CallsDay_CallsDay_ChargeDay_MinsEve_CallsEve_ChargeEve_MinsInternational_CallsInternational_ChargeInternational_MinsInternational_PlanNight_CallsNight_ChargeNight_MinsVMail_Plan
Account_Length1.0000.000-0.0040.0280.0090.0090.014-0.010-0.0100.0270.0100.0100.000-0.006-0.004-0.0040.000
Churn0.0001.0000.3150.0350.3520.3520.0000.0810.0810.0810.0560.0560.2560.0000.0250.0260.108
CustServ_Calls-0.0040.3151.000-0.014-0.013-0.0130.014-0.022-0.022-0.008-0.022-0.0220.026-0.003-0.019-0.0190.026
Day_Calls0.0280.035-0.0141.000-0.000-0.0000.012-0.003-0.0030.0090.0080.0080.000-0.0110.0110.0110.000
Day_Charge0.0090.352-0.013-0.0001.0001.0000.012-0.012-0.012-0.006-0.016-0.0160.0400.003-0.001-0.0010.032
Day_Mins0.0090.352-0.013-0.0001.0001.0000.012-0.012-0.012-0.006-0.016-0.0160.0390.003-0.001-0.0010.032
Eve_Calls0.0140.0000.0140.0120.0120.0121.0000.0000.0000.006-0.006-0.0060.000-0.0180.0100.0100.000
Eve_Charge-0.0100.081-0.022-0.003-0.012-0.0120.0001.0001.0000.0080.0090.0090.0000.014-0.018-0.0180.042
Eve_Mins-0.0100.081-0.022-0.003-0.012-0.0120.0001.0001.0000.0080.0090.0090.0060.014-0.018-0.0180.041
International_Calls0.0270.081-0.0080.009-0.006-0.0060.0060.0080.0081.0000.0080.0080.0000.002-0.009-0.0090.000
International_Charge0.0100.056-0.0220.008-0.016-0.016-0.0060.0090.0090.0081.0001.0000.005-0.001-0.004-0.0040.000
International_Mins0.0100.056-0.0220.008-0.016-0.016-0.0060.0090.0090.0081.0001.0000.005-0.001-0.004-0.0040.000
International_Plan0.0000.2560.0260.0000.0400.0390.0000.0000.0060.0000.0050.0051.0000.0000.0530.0530.000
Night_Calls-0.0060.000-0.003-0.0110.0030.003-0.0180.0140.0140.002-0.001-0.0010.0001.0000.0200.0210.000
Night_Charge-0.0040.025-0.0190.011-0.001-0.0010.010-0.018-0.018-0.009-0.004-0.0040.0530.0201.0001.0000.013
Night_Mins-0.0040.026-0.0190.011-0.001-0.0010.010-0.018-0.018-0.009-0.004-0.0040.0530.0211.0001.0000.012
VMail_Plan0.0000.1080.0260.0000.0320.0320.0000.0420.0410.0000.0000.0000.0000.0000.0130.0121.000

Missing values

2025-09-21T13:04:54.045508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-09-21T13:04:54.237288image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Account_LengthInternational_PlanVMail_PlanDay_MinsDay_CallsDay_ChargeEve_MinsEve_CallsEve_ChargeNight_MinsNight_CallsNight_ChargeInternational_MinsInternational_CallsInternational_ChargeCustServ_CallsChurn
0128noyes265.111045.07197.49916.78244.79111.0110.032.701False.
1107noyes161.612327.47195.510316.62254.410311.4513.733.701False.
2137nono243.411441.38121.211010.30162.61047.3212.253.290False.
384yesno299.47150.9061.9885.26196.9898.866.671.782False.
475yesno166.711328.34148.312212.61186.91218.4110.132.733False.
5118yesno223.49837.98220.610118.75203.91189.186.361.700False.
6121noyes218.28837.09348.510829.62212.61189.577.572.033False.
7147yesno157.07926.69103.1948.76211.8969.537.161.920False.
8117nono184.59731.37351.68029.89215.8909.718.742.351False.
9141yesyes258.68443.96222.011118.87326.49714.6911.253.020False.
Account_LengthInternational_PlanVMail_PlanDay_MinsDay_CallsDay_ChargeEve_MinsEve_CallsEve_ChargeNight_MinsNight_CallsNight_ChargeInternational_MinsInternational_CallsInternational_ChargeCustServ_CallsChurn
460776nono168.47428.63161.08313.69147.41176.638.042.161False.
460889noyes197.88433.63223.211718.97201.71329.085.741.541False.
4609128nono212.411836.11148.610812.63214.8989.679.852.651False.
4610138nono63.711410.83212.213218.04247.611411.148.842.380False.
461190nono193.89032.95206.69817.56153.31206.9010.192.733False.
461257noyes144.08124.48187.211215.91158.61227.148.562.303False.
4613177noyes189.09132.13303.19625.76163.61167.3615.714.243False.
461467noyes127.512621.68296.112925.17200.9919.0413.033.511False.
461598noyes168.99828.71226.311719.24165.5967.4514.333.860False.
4616140nono204.710034.80126.810710.78202.81159.1312.143.272False.